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Quantitative analysis is an increasingly essential skill for social science research, yet students in the social sciences and related areas typically receive little training in it—or if they do, they usually end up in statistics classes that offer few insights into their field. This textbook is a practical introduction to data analysis and statistics written especially for undergraduates and beginning graduate students in the social sciences and allied fields, such as economics, sociology, public policy, and data science. Quantitative Social Science engages directly with empirical analysis, showing students how to analyze data using the R programming language and to interpret the results—it encourages hands-on learning, not paper-and-pencil statistics. More than forty data sets taken directly from leading quantitative social science research illustrate how data analysis can be used to answer important questions about society and human behavior. Proven in the classroom, this one-of-a-kind textbook features numerous additional data analysis exercises and interactive R programming exercises, and also comes with supplementary teaching materials for instructors. Written especially for students in the social sciences and allied fields, including economics, sociology, public policy, and data science Provides hands-on instruction using R programming, not paper-and-pencil statistics Includes more than forty data sets from actual research for students to test their skills on Covers data analysis concepts such as causality, measurement, and prediction, as well as probability and statistical tools Features a wealth of supplementary exercises, including additional data analysis exercises and interactive programming exercises Offers a solid foundation for further study Comes with additional course materials online, including notes, sample code, exercises and problem sets with solutions, and lecture slides

An introductory textbook on data analysis and statistics written especially for students in the social sciences and allied fields Quantitative analysis is an increasingly essential skill for social science research, yet students in the social sciences and related areas typically receive little training in it--or if they do, they usually end up in statistics classes that offer few insights into their field. This textbook is a practical introduction to data analysis and statistics written especially for undergraduates and beginning graduate students in the social sciences and allied fields, such as economics, sociology, public policy, and data science. Quantitative Social Science engages directly with empirical analysis, showing students how to analyze data using the R programming language and to interpret the results--it encourages hands-on learning, not paper-and-pencil statistics. More than forty data sets taken directly from leading quantitative social science research illustrate how data analysis can be used to answer important questions about society and human behavior. Proven in the classroom, this one-of-a-kind textbook features numerous additional data analysis exercises and interactive R programming exercises, and also comes with supplementary teaching materials for instructors. Written especially for students in the social sciences and allied fields, including economics, sociology, public policy, and data science Provides hands-on instruction using R programming, not paper-and-pencil statistics Includes more than forty data sets from actual research for students to test their skills on Covers data analysis concepts such as causality, measurement, and prediction, as well as probability and statistical tools Features a wealth of supplementary exercises, including additional data analysis exercises and interactive programming exercises Offers a solid foundation for further study Comes with additional course materials online, including notes, sample code, exercises and problem sets with solutions, and lecture slides

Quantitative analysis is an increasingly essential skill for social science research, yet students in the social sciences and related areas typically receive little training in it--or if they do, they usually end up in statistics classes that offer few insights into their field. This textbook is a practical introduction to data analysis and statistics written especially for undergraduates and beginning graduate students in the social sciences and allied fields, such as economics, sociology, public policy, and data science. Quantitative Social Science engages directly with empirical analysis, showing students how to analyze data using the R programming language and to interpret the results--it encourages hands-on learning, not paper-and-pencil statistics. More than forty data sets taken directly from leading quantitative social science research illustrate how data analysis can be used to answer important questions about society and human behavior. Proven in the classroom, this one-of-a-kind textbook features numerous additional data analysis exercises and interactive R programming exercises, and also comes with supplementary teaching materials for instructors. Written especially for students in the social sciences and allied fields, including economics, sociology, public policy, and data science Provides hands-on instruction using R programming, not paper-and-pencil statistics Includes more than forty data sets from actual research for students to test their skills on Covers data analysis concepts such as causality, measurement, and prediction, as well as probability and statistical tools Features a wealth of supplementary exercises, including additional data analysis exercises and interactive programming exercises Offers a solid foundation for further study Comes with additional course materials online, including notes, sample code, exercises and problem sets with solutions, and lecture slides

This original textbook provides a comprehensive and integrated approach to using quantitative methods in the social sciences. Thomas R Black guides the student and researcher through the minefield of potential problems that may be confronted, and it is this emphasis on the practical that distinguishes his book from others which focus exclusively on either research design and measurement or statistical methods. Focusing on the design and execution of research, key topics such as planning, sampling, the design of measuring instruments, choice of statistical text and interpretation of results are examined within the context of the research process. In a lively and accessible style, the student is introduced to researc design issues alongside statistical procedures and encouraged to develop analytical and decision-making skills.

The SAGE Handbook of Quantitative Methodology for the Social Sciences is the definitive reference for teachers, students, and researchers of quantitative methods in the social sciences, as it provides a comprehensive overview of the major techniques used in the field. The contributors, top methodologists and researchers, have written about their areas of expertise in ways that convey the utility of their respective techniques, but, where appropriate, they also offer a fair critique of these techniques. Relevance to real-world problems in the social sciences is an essential ingredient of each chapter and makes this an invaluable resource.

Do you have data that is not normally distributed and don't know how to analyze it using generalized linear models (GLM)? Beginning with a discussion of fundamental statistical modeling concepts in a multiple regression framework, the authors extend these concepts to GLM and demonstrate the similarity of various regression models to GLM. Each procedure is illustrated using real life data sets. The book provides an accessible but thorough introduction to GLM, exponential family distribution, and maximum likelihood estimation; includes discussion on checking model adequacy and description on how to use SAS to fit GLM; and describes the connection between survival analysis and GLM. It is an ideal text for social science researchers who do not have a strong statistical background, but would like to learn more advanced techniques having taken an introductory course covering regression analysis.

This volume introduces the basic concepts of Exponential Random Graph Modeling (ERGM), gives examples of why it is used, and shows the reader how to conduct basic ERGM analyses in their own research. ERGM is a statistical approach to modeling social network structure that goes beyond the descriptive methods conventionally used in social network analysis. Although it was developed to handle the inherent non-independence of network data, the results of ERGM are interpreted in similar ways to logistic regression, making this a very useful method for examining social systems. Recent advances in statistical software have helped make ERGM accessible to social scientists, but a concise guide to using ERGM has been lacking. An Introduction to Exponential Random Graph Modeling, by Jenine K. Harris, fills that gap, by using examples from public health, and walking the reader through the process of ERGM model-building using R statistical software and the statnet package.

This volume introduces the reader to one of the most fundamental topics in social science statistics: experimental design. The authors clearly show how to select an experimental design based on the number of independent variables and the number of subjects. Other topics addressed include variability, hypothesis testing, how ANOVA can be extended to the multi-group situation, the logic of the t test and completely randomized designs.

How do we group different subjects on a variety of variables? Should we use a classification procedure in which only the concepts are classified (typology), one in which only empirical entities are classified (taxonomy), or a combination of both? Kenneth D Bailey addresses these questions and shows how classification methods can be used to improve research. Beginning with an exploration of the advantages and disadvantages of classification procedures, the book covers topics such as: clustering procedures including agglomerative and divisive methods; the relationship among various classification techniques; how clustering methods compare with related statistical techniques; classification resources; and software packages fo

With Regression Diagnostics, researchers now have an accessible explanation of the techniques needed for exploring problems that compromise a regression analysis and for determining whether certain assumptions appear reasonable. The book covers such topics as the problem of collinearity in multiple regression, dealing with outlying and influential data, non-normality of errors, non-constant error variance and the problems and opportunities presented by discrete data. In addition, sophisticated diagnostics based on maximum-likelihood methods, scores tests, and constructed variables are introduced.

This book offers an overview of the central ideas in calculus and gives examples of how calculus is used to translate many real-world phenomena into mathematical functions. Beginning with an explanation of the two major parts of calculus - differentiation and integration - Gudmund R Iversen illustrates how calculus is used in statistics: to distinguish between the mean and the median; to derive the least squares formulas for regression co-efficients; to find values of parameters from theoretical distributions; and to find a statistical p-value when using one of the continuous test variables such as the t-variable.

This volume offers students a basic introduction to assessing the meaning and validity of research in the social sciences and related fields. The ability to "read "published research critically is essential and is different from the skills involved in "undertaking "research using statistical analysis. Thomas R Black explains in clear and straightforward terms how students can evaluate research, with particular emphasis on research involving some aspect of measurement. The coverage of fundamental concepts is comprehensive and supports topics including research design, data collection and data analysis by addressing the following major issues: Are the questions and hypotheses advanced appropriate and testable? Is the research design sufficient for the hypothesis? Are the data gathered valid, reliable and objective? Are the statistical techniques used to analyze the data appropriate and do they support the conclusions reached?

Political science and sociology increasingly rely on mathematical modeling and sophisticated data analysis, and many graduate programs in these fields now require students to take a "math camp" or a semester-long or yearlong course to acquire the necessary skills. Available textbooks are written for mathematics or economics majors, and fail to convey to students of political science and sociology the reasons for learning often-abstract mathematical concepts. A Mathematics Course for Political and Social Research fills this gap, providing both a primer for math novices in the social sciences and a handy reference for seasoned researchers. The book begins with the fundamental building blocks of mathematics and basic algebra, then goes on to cover essential subjects such as calculus in one and more than one variable, including optimization, constrained optimization, and implicit functions; linear algebra, including Markov chains and eigenvectors; and probability. It describes the intermediate steps most other textbooks leave out, features numerous exercises throughout, and grounds all concepts by illustrating their use and importance in political science and sociology. Uniquely designed and ideal for students and researchers in political science and sociology Uses practical examples from political science and sociology Features "Why Do I Care?" sections that explain why concepts are useful Includes numerous exercises Complete online solutions manual (available only to professors, email david.siegel at duke.edu, subject line "Solution Set") Selected solutions available online to students

Both Traditional Students and Working Professionals Acquire the Skills to Analyze Social Problems. Big Data and Social Science: A Practical Guide to Methods and Tools shows how to apply data science to real-world problems in both research and the practice. The book provides practical guidance on combining methods and tools from computer science, statistics, and social science. This concrete approach is illustrated throughout using an important national problem, the quantitative study of innovation. The text draws on the expertise of prominent leaders in statistics, the social sciences, data science, and computer science to teach students how to use modern social science research principles as well as the best analytical and computational tools. It uses a real-world challenge to introduce how these tools are used to identify and capture appropriate data, apply data science models and tools to that data, and recognize and respond to data errors and limitations. For more information, including sample chapters and news, please visit the author's website.

"In an age of big data, data journalism and with a wealth of quantitative information around us, it is not enough for students to be taught only 100 year old statistical methods using 'out of the box' software. They need to have 21st-century analytical skills too. This is an excellent and student-friendly text from two of the world leaders in the teaching and development of spatial analysis. It shows clearly why the open source software R is not just an alternative to commercial GIS, it may actually be the better choice for mapping, analysis and for replicable research. Providing practical tips as well as fully working code, this is a practical 'how to' guide ideal for undergraduates as well as those using R for the first time. It will be required reading on my own courses." - Richard Harris, Professor of Quantitative Social Science, University of Bristol R is a powerful open source computing tool that supports geographical analysis and mapping for the many geography and ‘non-geography’ students and researchers interested in spatial analysis and mapping. This book provides an introduction to the use of R for spatial statistical analysis, geocomputation and the analysis of geographical information for researchers collecting and using data with location attached, largely through increased GPS functionality. Brunsdon and Comber take readers from ‘zero to hero’ in spatial analysis and mapping through functions they have developed and compiled into R packages. This enables practical R applications in GIS, spatial analyses, spatial statistics, mapping, and web-scraping. Each chapter includes: Example data and commands for exploring it Scripts and coding to exemplify specific functionality Advice for developing greater understanding - through functions such as locator(), View(), and alternative coding to achieve the same ends Self-contained exercises for students to work through Embedded code within the descriptive text. This is a definitive 'how to' that takes students - of any discipline - from coding to actual applications and uses of R.

In the new edition of Introduction to Social Research, Keith Punch takes a fresh look at the entire research process, from formulating a research question to writing up your research. Covering qualitative, quantitative and mixed methods, the book focuses on matching research questions to appropriate methods. Offering concise, balanced coverage, this book clearly explains the underlying principles of social research and shows you how to put this understanding into practice. The third edition includes: A new chapter on literature searching and reviewing Expanded coverage of ethics A new section on using the internet in research A range of additional student learning features A brand new companion website including full-text journal articles, additional case studies and video tutorials. Using a range of examples from student research and published work, the book is an ideal introduction for any social science student taking a research methods course or embarking on their own undergraduate or postgraduate research project. Available with Perusall—an eBook that makes it easier to prepare for class Perusall is an award-winning eBook platform featuring social annotation tools that allow students and instructors to collaboratively mark up and discuss their SAGE textbook. Backed by research and supported by technological innovations developed at Harvard University, this process of learning through collaborative annotation keeps your students engaged and makes teaching easier and more effective. Learn more.